A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction. (March 2015)

• Record Type: Journal Article
• Authors: Salcedo-Sanz, S.; Pastor-Sánchez, A.; Del Ser, J.; Prieto, L.; Geem, Z.W.
• Is Part Of: Renewable energy. Volume 75(2015)
• Page Start: 93
• Page End: 101
• Publication Date: 2015-03
• Subjects: Short term wind speed prediction -- Feature selection -- Coral Reefs Optimization -- Harmony Search -- Extreme learning machines; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2014.09.027
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bio-inspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to naïve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines. Highlights: A novel approach for short-term wind speed prediction is presented. The system proposed is formed by a CRO algorithm and an ELM neurnal network. Feature Selection is carried out with the CRO to improve the ELM performance. The hybrid scheme is validated in meteorological towers located in USA and Spain. Results are compared to those by naïve versions of its constituent algorithms.